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Ambient gamma radiation is a key component of environmental radiation monitoring and is strongly modulated by atmospheric and meteorological processes. This study presents a long-term analysis of near-surface gamma radiation measured in Ponta Delgada (São Miguel Island, Azores), integrating continuous observations from the Portuguese National Alert Network for Environmental Radioactivity (RADNET) with meteorological data. The dataset spans more than a decade and includes a documented instrumental upgrade in 2020, which introduced enhanced sensitivity and radionuclide identification capability. Results reveal pronounced variability across daily, seasonal, and interannual timescales. Stepwise level shifts are identified in 2016 and 2020, associated with operational and instrumental modifications, respectively, rather than with changes in environmental radioactivity. Seasonal analysis shows higher gamma radiation values during autumn and winter and lower values in late spring and summer, consistent with precipitation-driven washout and boundary-layer dynamics. Generalized Additive Models (GAMs) highlight precipitation, wind speed, and relative humidity as dominant meteorological drivers acting through non-linear relationships. Overall, the results support the use of ambient gamma radiation as an atmospheric indicator of boundary-layer processes and meteorological modulation in remote maritime environments, extending its role beyond routine environmental surveillance.

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection gap. Building on the methodological framework of existing actuarial climate indices, previously adapted for France and the Iberian Peninsula, the index integrates six standardised indicators capturing warm and cool temperature extremes, heavy precipitation intensity, dry spell duration, high wind frequency, and sea level change. It leverages hourly ERA5-Land reanalysis data and monthly sea level observations from tide gauges. Results show a clear upward trend in climate anomalies, with regional and seasonal differentiation. Among all components, sea level is most strongly correlated with the composite index, underscoring Italy’s vulnerability to marine-related risks. Comparative analysis with European indices confirms both the robustness and specificity of the Italian exposure profile, reinforcing the need for tailored risk metrics. The index can support innovative risk transfer mechanisms, including climate-related insurance, regulatory stress testing, and resilience planning. Combining scientific rigour with operational relevance, it offers a consistent, transparent, and policy-relevant tool for managing climate risk in Italy and contributing to harmonised European frameworks.

Humans have significantly altered the energy balance of the Earth’s climate system mainly not only by extracting and burning fossil fuels but also by altering the biosphere and using halocarbons. The 3rd US National Climate Assessment pointed to a need for a system of indicators of climate and global change based on long-term data that could be used to support assessments and this led to the development of the National Climate Indicators System (NCIS). Here we identify a representative set of key atmospheric indicators of changes in atmospheric radiative forcing due to greenhouse gases (GHGs), and we evaluate atmospheric composition measurements, including non-CO2GHGs for use as climate change indicators in support of the US National Climate Assessment. GHG abundances and their changes over time can provide valuable information on the success of climate mitigation policies, as well as insights into possible carbon-climate feedback processes that may ultimately affect the success of those policies. To ensure that reliable information for assessing GHG emission changes can be provided on policy-relevant scales, expanded observational efforts are needed. Furthermore, the ability to detect trends resulting from changing emissions requires a commitment to supporting long-term observations. Long-term measurements of greenhouse gases, aerosols, and clouds and related climate indicators used with a dimming/brightening index could provide a foundation for quantifying forcing and its attribution and reducing error in existing indicators that do not account for complicated cloud processes.

We present an overview of results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33) on the viability of large-scale deployment of bioenergy for achieving long-run climate goals. The study explores future bioenergy use across models under harmonized scenarios for future climate policies, availability of bioenergy technologies, and constraints on biomass supply. This paper provides a more transparent description of IAMs that span a broad range of assumptions regarding model structures, energy sectors, and bioenergy conversion chains. Without emission constraints, we find vastly different CO2 emission and bioenergy deployment patterns across models due to differences in competition with fossil fuels, the possibility to produce large-scale bio-liquids, and the flexibility of energy systems. Imposing increasingly stringent carbon budgets mostly increases bioenergy use. A diverse set of available bioenergy technology portfolios provides flexibility to allocate bioenergy to supply different final energy as well as remove carbon dioxide from the atmosphere by combining bioenergy with carbon capture and sequestration (BECCS). Sector and regional bioenergy allocation varies dramatically across models mainly due to bioenergy technology availability and costs, final energy patterns, and availability of alternative decarbonization options. Although much bioenergy is used in combination with CCS, BECCS is not necessarily the driver of bioenergy use. We find that the flexibility to use biomass feedstocks in different energy sub-sectors makes large-scale bioenergy deployment a robust strategy in mitigation scenarios that is surprisingly insensitive with respect to reduced technology availability. However, the achievability of stringent carbon budgets and associated carbon prices is sensitive. Constraints on biomass feedstock supply increase the carbon price less significantly than excluding BECCS because carbon removals are still realized and valued. Incremental sensitivity tests find that delayed readiness of bioenergy technologies until 2050 is more important than potentially higher investment costs.

Evaluating the quality of ecosystems in terms of biological patrimony and functioning is of critical importance in the actual context of intensified human activities. Microbial diversity is commonly used as a bioindicator of ecosystems functioning. However, there is a lack of sensitivity of microbial diversity indicators in the case of moderate and chronic environmental degradation, such as atmospheric deposition of pollutants, agricultural practices, diffuse pollution by wastewater and climate change. As a consequence, there is a need for alternative bioindicators of soils and water quality. Here, we discuss the interest of adopting a more integrative approach based on biotic interaction networks beyond the simple diversity indicators. We review how the various biotic interactions can be integrated in the various microbial networks such as trophic, mutualistic and co-occurrence networks. Then we discuss the efficiency of microbial networks and associated metrics to detect changes in microbial communities. We conclude that the connectance, the number of links and the average degree of co-occurrence networks could vary from 10 to 50% in response to minor perturbations when microbial diversity parameters remain stable. Finally, we analyze studies that aimed at linking microbial networks and activity to evaluate the potential of such networks for providing simple and operational indicators of ecosystem quality and functioning.

Measurement of the abundance of atmospheric carbon dioxide as an indicator of air pollution has been of very limited value because of variations in urban areas in the substantial concentration of natural carbon dioxide produced from combustion and noncombustion (natural) sources. A solution to this problem is the use of precise isotopic assay of ratios of carbon-13 to carbon-12 in atmospheric carbon dioxide. There is very little variation of carbon isotopic composition in samples taken over rural or urban areas where rapid mixing and diffusion of gaseous combustion products is possible. Significant differences in this composition in samples taken at centrally located points at street level in the lower Manhattan business district show an increase in concentration of atmospheric carbon dioxide of roughly 20 percent produced primarily by automobile exhaust.

The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.

Climate change affects all segments of the agricultural enterprise, and there is mounting evidence that the continuing warming trend with shifting seasonality and intensity in precipitation will increase the vulnerability of agricultural systems. Agricultural is a complex system within the USA encompassing a large number of crops and livestock systems, and development of indicators to provide a signal of the impact of climate change on these different systems would be beneficial to the development of strategies for effective adaptation practices. A series of indicators were assembled to determine their potential for assessing agricultural response to climate change in the near term and long term and those with immediate capability of being implemented and those requiring more development. The available literature reveals indicators on livestock related to heat stress, soil erosion related to changes in precipitation, soil carbon changes in response to increasing carbon dioxide and soil management practices, economic response to climate change in agricultural production, and crop progress and productivity. Crop progress and productivity changes are readily observed data with a historical record for some crops extending back to the mid-1800s. This length of historical record coupled with the county-level observations from each state where a crop is grown and emerging pest populations provides a detailed set of observations to assess the impact of a changing climate on agriculture. Continued refinement of tools to assess climate impacts on agriculture will provide guidance on strategies to adapt to climate change.

In recent years, the Southern Ocean has experienced extremely low sea ice cover in multiple summers. These low events were preceded by a multidecadal positive trend that culminated in record high ice coverage in 2014. This abrupt transition has led some authors to suggest that Antarctic sea ice has undergone a regime shift. In this study we analyze the satellite sea ice record and atmospheric reanalyses to assess the evidence for such a shift. We find that the standard deviation of the summer sea ice record has doubled from 0.31 million km 2 in 1979–2006 to 0.76 million km 2 for 2007–22. This increased variance is accompanied by a longer season-to-season sea ice memory. The atmosphere is the primary driver of Antarctic sea ice variability, but using a linear predictive model we show that sea ice changes cannot be explained by the atmosphere alone. Identifying whether a regime shift has occurred is difficult without a complete understanding of the physical mechanism of change. However, the statistical changes that we demonstrate (i.e., increased variance and autocorrelation, and a changed response to atmospheric forcing), as well as the increased spatial coherence noted by previous research, are indicators based on dynamical systems theory of an abrupt critical transition. Thus, our analysis is further evidence in support of a changed Antarctic sea ice system.

Microfibers are now ubiquitous in the environment largely due to the widespread use of natural and synthetic textiles. Many enter aquatic systems through wastewater treatment plant (WWTP) effluent, surface water runoff, and atmospheric deposition, where they persist and may be ingested by filter-feeding organisms. In addition to causing physical damage (e.g., digestive and respiratory obstructions), microfibers are often carriers of chemical pollutants that may also harm biota. This exploratory study aimed to determine whether freshwater mussel (Margaritifera margaritifera L.) microfiber content varied between two rural tributaries of the Saint John River, whether microfiber content was related to WWTP discharge points or potential diffuse microfiber sources, and whether mussel size was associated with microfiber content. Mussels were collected both upstream and downstream of five WWTP discharge points and at 11 other points along two rivers within rural watersheds of maritime Canada. Microfiber content differed significantly between the two rivers; however, no trends were observed in microfiber content in relation to WWTP discharge points on either river. Smaller mussels contained significantly more microfibers than larger mussels, despite differences in mussel size ranges between tributaries. These results reveal a potential pathway for microfibers to enter aquatic food webs and highlight important implications for the use of freshwater mussels as bioindicators of microfiber contamination.